TY - JOUR
ID - 247577
TI - Comparing Methods of Determining Test Factor Structure Using Empirical data: The Case of National Entrance Exam in 2016
JO - Educational Measurement and Evaluation Studies
JA - EMES
LA - en
SN - 2476-2865
AU - Izanloo, Balal
AD -
Y1 - 2021
PY - 2021
VL - 11
IS - 33
SP - 27
EP - 58
KW - Dimension
KW - Factor
KW - construct
KW - factor structure
KW - Multiple Choice Test
KW - binary data
DO - 10.22034/emes.2021.247577
N2 - Objective: The present study aimed to compare the dimensionality assessment methods using National Entrance Exam data and determine the number of dimensions in the examâ€™s data.Methods: The data from mathematics (mathematics group), chemistry (experimental sciences group) and Philosophy-logic (humanities group) sub-tests of the National Entrance Exam in 2016 AD (1395 solar) were used for analysis.Results: Analysis based on 11 methods resulted in 34 related indices and graphical methods, such as hierarchical cluster analysis, exploratory graph analysis and heat map revealed that different methods, depending on their nature, resulted in general factors, specific factors, and a cluster of items. Results showed that the required uni-dimensionality did not exist in most cases, and the structure of the specialized national exam in 2016 was bi-factorial. The only difference was that the resulting bi-factor structure did not match the specifications of the previous bi-factor model (i.e., a general factor and several specific factors unrelated to each other and the general factor, so that each item is merely related to one specific factor in addition to the general factor). In other words, besides correlating with the general factor, each item is related to more than one specific factor whose result was a complex or a relatively complex structure. Factor analysis of the total data and nonlinear factor analysis revealed that a gradual increase in lower asymptote reduced the number of dimensions. Conclusion: It is recommended to apply a combination of methods to find the dimensions of the National Entrance Exam. In addition, the extent of general factor saturation, reflected in item correlations, considering lower asymptote, the way of dealing with omitted responses in analysis, and comparing results of all data with complete data (data without missing values) can be useful for dimensionality assessment. Furthermore, researchers should consider checking the fit of the models extracted from different explanatory methods by confirmatory factor analysis and the interpretation of the extracted model.
UR - https://jresearch.sanjesh.org/article_247577.html
L1 - https://jresearch.sanjesh.org/article_247577_85e3f0aef356d7f6c049e31627064359.pdf
ER -